Bayesian model selection with fractional Brownian motion

2018/04/04 by Krog, Jens, Jacobsen, Lars H., Lund, Frederik W. +2 · 3 citations
#Data Analysis #FOS: Physical sciences #Statistical Mechanics (cond-mat.stat-mech) #Statistics and Probability (physics.data-an)

paper · doi:10.48550/arxiv.1804.01365

Abstract

We implement Bayesian model selection and parameter estimation for the case of fractional Brownian motion with measurement noise and a constant drift. The approach is tested on artificial trajectories and shown to make estimates that match well with the underlying true parameters, while for model selection the approach has a preference for simple models when the trajectories are finite. The approach is applied to observed trajectories of vesicles diffusing in Chinese hamster ovary cells. Here it is supplemented with a goodness-of-fit test, which is able to reveal statistical discrepancies between the observed trajectories and model predictions.

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